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Fix typos #2064

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Oct 3, 2022
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Fix typos #2064

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4 changes: 2 additions & 2 deletions beginner_source/introyt/autogradyt_tutorial.py
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Original file line number Diff line number Diff line change
Expand Up @@ -40,7 +40,7 @@

###########################################################################
# A machine learning model is a *function*, with inputs and outputs. For
# this discussion, we’ll treat the inputs a as an *i*-dimensional vector
# this discussion, we’ll treat the inputs as an *i*-dimensional vector
# :math:`\vec{x}`, with elements :math:`x_{i}`. We can then express the
# model, *M*, as a vector-valued function of the input: :math:`\vec{y} =
# \vec{M}(\vec{x})`. (We treat the value of M’s output as
Expand Down Expand Up @@ -226,7 +226,7 @@
# of which should be :math:`2 * cos(a)`. Looking at the graph above,
# that’s just what we see.
#
# Be aware than only *leaf nodes* of the computation have their gradients
# Be aware that only *leaf nodes* of the computation have their gradients
# computed. If you tried, for example, ``print(c.grad)`` you’d get back
# ``None``. In this simple example, only the input is a leaf node, so only
# it has gradients computed.
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